Update app.py

app.py

@@ -1,83 +1,40 @@
 import gradio as gr
-import torch
 from transformers import pipeline
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import random
 
-# Load AI models
-symptom_model = pipeline("text-classification", model="nlp-ai/symptom-disease")  # Replace with trained model
-emotion_model = pipeline("sentiment-analysis")  # Pre-trained NLP for emotions
-retina_model = tf.keras.models.load_model("retina_disease_model.h5")  # Custom trained Retina AI
-stool_model = tf.keras.models.load_model("stool_microbiome_model.h5")  # Gut microbiome detection AI
+# Load models
+emotion_model = pipeline("text-classification", model="bert-base-uncased")
+microbiome_model = pipeline("text-generation", model="microsoft/BioGPT-Large")
+retina_model = pipeline("image-classification", model="microsoft/resnet-50")
 
-# Function for AI-powered diagnosis
-def diagnose(symptoms):
-    result = symptom_model(symptoms)[0]
-    return f"Condition: {result['label']} (Confidence: {round(result['score'] * 100, 2)}%)"
+# Define functions
+def diagnose_emotion(text):
+    return emotion_model(text)
 
-# Function for stool image microbiome analysis
-def analyze_stool(image):
-    img = Image.open(image).resize((224, 224))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    
-    prediction = stool_model.predict(img_array)
-    class_names = ["Healthy Gut", "Possible Dysbiosis", "Severe Gut Imbalance"]
-    return f"Result: {class_names[np.argmax(prediction)]}"
+def analyze_microbiome(symptoms):
+    return microbiome_model(symptoms)
 
-# Retina disease detection function
 def analyze_retina(image):
-    img = Image.open(image).resize((224, 224))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-
-    prediction = retina_model.predict(img_array)
-    class_names = ["Normal Retina", "Diabetic Retinopathy", "Glaucoma Detected"]
-    return f"Retina Scan: {class_names[np.argmax(prediction)]}"
-
-# Emotion-to-disease analysis
-def emotion_to_disease(emotion_text):
-    emotions = {
-        "stress": "Long-term stress can cause high blood pressure, anxiety, and digestive issues.",
-        "anger": "Frequent anger can increase heart disease risk.",
-        "happiness": "A positive mindset improves overall well-being!"
-    }
-    detected_emotion = emotion_model(emotion_text)[0]['label']
-    return emotions.get(detected_emotion.lower(), "No specific health risks detected.")
+    return retina_model(image)
 
 # Gradio UI
-with gr.Blocks(theme="soft") as demo:
-    gr.Markdown("# 🏥 Diagnosify-AI: AI-powered Health & Disease Detection")
-
-    # Symptom diagnosis
-    with gr.Row():
-        symptom_input = gr.Textbox(label="Enter symptoms")
-        symptom_output = gr.Textbox(label="Diagnosis Result", interactive=False)
-        gr.Button("Diagnose").click(diagnose, inputs=symptom_input, outputs=symptom_output)
-
-    # Gut microbiome (stool analysis)
-    with gr.Row():
-        stool_input = gr.Image(label="Upload Stool Image")
-        stool_output = gr.Textbox(label="Microbiome Analysis", interactive=False)
-        gr.Button("Analyze Stool").click(analyze_stool, inputs=stool_input, outputs=stool_output)
+with gr.Blocks() as app:
+    gr.Markdown("# Diagnosify-AI - AI Medical Assistant")
+    text_input = gr.Textbox(label="Enter Symptoms")
+    image_input = gr.Image(type="pil", label="Upload Retina Scan")
 
-    # Retina scan
-    with gr.Row():
-        retina_input = gr.Image(label="Upload Retina Image")
-        retina_output = gr.Textbox(label="Retina Disease Detection", interactive=False)
-        gr.Button("Analyze Retina").click(analyze_retina, inputs=retina_input, outputs=retina_output)
+    btn1 = gr.Button("Diagnose Emotion-based Disease")
+    btn2 = gr.Button("Analyze Gut Health")
+    btn3 = gr.Button("Detect Retinal Disease")
 
-    # Emotion-to-Disease
-    with gr.Row():
-        emotion_input = gr.Textbox(label="Describe your emotions")
-        emotion_output = gr.Textbox(label="Health Analysis", interactive=False)
-        gr.Button("Analyze Emotion").click(emotion_to_disease, inputs=emotion_input, outputs=emotion_output)
+    output1 = gr.Textbox(label="Diagnosis")
+    output2 = gr.Textbox(label="Microbiome Analysis")
+    output3 = gr.Label(label="Retinal Disease Prediction")
 
-    gr.Markdown("🔗 **Diagnosify-AI** - Bringing AI-powered diagnostics to your fingertips!")
+    btn1.click(diagnose_emotion, inputs=text_input, outputs=output1)
+    btn2.click(analyze_microbiome, inputs=text_input, outputs=output2)
+    btn3.click(analyze_retina, inputs=image_input, outputs=output3)
 
 # Launch the app
-demo.launch()
+app.launch()